A Bottom-Up Approach for Licences Classification and Selection

Daga, Enrico; d’Aquin, Mathieu; Motta, Enrico; Gangemi, Aldo

doi:10.1007/978-3-319-25639-9_41

Cited by 15 publications

(17 citation statements)

References 6 publications

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“…We achieve this by applying FCA, implemented in the Contento tool [4]. The output of the FCA algorithm is an ordered set of concepts C. In FCA terms, each concept groups a set of objects (the concept's extent) and maps it to a set of attributes (the concept's intent).…”

Section: Discussionmentioning

confidence: 99%

“…In our case we use it to detect common behavior of 3 https://www.w3.org/community/odrl/ relations in terms of policy propagation, and then to test and refine the Datanode ontology. In [4] FCA has been applied as part of a method for classifying licences based on common features, to reduce the cost of licence selection. We refer the reader to [4] for a description of the Contento tool, that implements FCA as well as other functionalities for evolving concept lattices in Semantic Web ontologies, also part of the approach we present here.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The two collections have been used together in the Contento tool [4], that generated the FCA formal context, i.e. a binary matrix of 12995 cells.…”

Section: Acquisitionmentioning

confidence: 99%

“…In fact, looking at the intent of Concept 74 in Listing 5, it sounds reasonable that all the parts of a given data objects inherit all the cited duties 4 . The full precision enables the rules reduction process.…”

Section: Abstractionmentioning

confidence: 99%

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Propagation of Policies in Rich Data Flows

Daga

d’Aquin

Gangemi

et al. 2015

Proceedings of the 8th International Conference on Knowledge Capture

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Governing the life cycle of data on the web is a challenging issue for organisations and users. Data is distributed under certain policies that determine what actions are allowed and in which circumstances. Assessing what policies propagate to the output of a process is one crucial problem. Having a description of policies and data flow steps implies a huge number of propagation rules to be specified and computed (number of policies times number of actions). In this paper we provide a method to obtain an abstraction that allows to reduce the number of rules significantly. We use the Datanode ontology, a hierarchical organisation of the possible relations between data objects, to compact the knowledge base to a set of more abstract rules. After giving a definition of Policy Propagation Rule, we show (1) a methodology to abstract policy propagation rules based on an ontology, (2) how effective this methodology is when using the Datanode ontology, (3) how this ontology can evolve in order to better represent the behaviour of policy propagation rules.

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Section: Discussionmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

“…The two collections have been used together in the Contento tool [4], that generated the FCA formal context, i.e. a binary matrix of 12995 cells.…”

Section: Acquisitionmentioning

confidence: 99%

Section: Abstractionmentioning

confidence: 99%

See 2 more Smart Citations

Propagation of Policies in Rich Data Flows

Daga

d’Aquin

Gangemi

et al. 2015

Proceedings of the 8th International Conference on Knowledge Capture

Self Cite

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“…For example, Creative Commons currently provides two methods for integrating license selection into applications: the Partner Interface and the web service API. In (Daga et al 2015), an ontology-based tool is presented for a data provider to select the license for their data. However, the selected licenses must obey the wish and intention of the data owner who, in the end, owns the rights to the data (Boris et al 2016).…”

Section: Limitations Open Issues Future Research Itemsmentioning

confidence: 99%

Towards certified open data in digital service ecosystems

2017

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The opportunities of open data have been recently recognized among companies in different domains. Digital service providers have increasingly been interested in the possibilities of innovating new ideas and services around open data. Digital service ecosystems provide several advantages for service developers, enabling the service co-innovation and co-creation among ecosystem members utilizing and sharing common assets and knowledge.The utilization of open data in digital services requires new innovation practices, service development models, and a collaboration environment. These can be provided by the ecosystem. However, since open data can be almost anything and originate from different kinds of data sources, the quality of data becomes the key issue. The new challenge for service providers is how to guarantee the quality of open data. In the ecosystems, uncertain data quality poses major challenges. The main contribution of this paper is the concept of the Evolvable Open Data based digital service Ecosystem (EODE), which defines the kinds of knowledge and services that are required for validating open data in digital service ecosystems. Thus, the EODE provides business potential for open data and digital service providers, as well as other actors around open data. The ecosystem capability model, knowledge management models, and the taxonomy of services to support the open data quality certification are described. Data quality certification confirms that the open data is trustworthy and its quality is good enough to be accepted for the usage of the ecosystem's services. The five-phase open data quality certification process, according to which open data is brought to the ecosystem and certified for the usage of the digital service ecosystem members using the knowledge models and support services of the ecosystem, is also described. The initial experiences of the Software Qual J (2018)

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